DonorsChoose

DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.

Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:

  • How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
  • How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
  • How to focus volunteer time on the applications that need the most assistance

The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.

About the DonorsChoose Data Set

The train.csv data set provided by DonorsChoose contains the following features:

Feature Description
project_id A unique identifier for the proposed project. Example: p036502
project_title Title of the project. Examples:
  • Art Will Make You Happy!
  • First Grade Fun
project_grade_category Grade level of students for which the project is targeted. One of the following enumerated values:
  • Grades PreK-2
  • Grades 3-5
  • Grades 6-8
  • Grades 9-12
project_subject_categories One or more (comma-separated) subject categories for the project from the following enumerated list of values:
  • Applied Learning
  • Care & Hunger
  • Health & Sports
  • History & Civics
  • Literacy & Language
  • Math & Science
  • Music & The Arts
  • Special Needs
  • Warmth

Examples:
  • Music & The Arts
  • Literacy & Language, Math & Science
school_state State where school is located (Two-letter U.S. postal code). Example: WY
project_subject_subcategories One or more (comma-separated) subject subcategories for the project. Examples:
  • Literacy
  • Literature & Writing, Social Sciences
project_resource_summary An explanation of the resources needed for the project. Example:
  • My students need hands on literacy materials to manage sensory needs!
project_essay_1 First application essay*
project_essay_2 Second application essay*
project_essay_3 Third application essay*
project_essay_4 Fourth application essay*
project_submitted_datetime Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245
teacher_id A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56
teacher_prefix Teacher's title. One of the following enumerated values:
  • nan
  • Dr.
  • Mr.
  • Mrs.
  • Ms.
  • Teacher.
teacher_number_of_previously_posted_projects Number of project applications previously submitted by the same teacher. Example: 2

* See the section Notes on the Essay Data for more details about these features.

Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:

Feature Description
id A project_id value from the train.csv file. Example: p036502
description Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25
quantity Quantity of the resource required. Example: 3
price Price of the resource required. Example: 9.95

Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:

The data set contains the following label (the value you will attempt to predict):

Label Description
project_is_approved A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved.

Notes on the Essay Data

    Prior to May 17, 2016, the prompts for the essays were as follows:
  • __project_essay_1:__ "Introduce us to your classroom"
  • __project_essay_2:__ "Tell us more about your students"
  • __project_essay_3:__ "Describe how your students will use the materials you're requesting"
  • __project_essay_3:__ "Close by sharing why your project will make a difference"
    Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
  • __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
  • __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"

  • For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.
In [1]:
import os
os.chdir("F:/f/MY____/AAIC/AssignmentS/DonorsChoose_Data")
os.getcwd()
Out[1]:
'F:\\f\\MY____\\AAIC\\AssignmentS\\DonorsChoose_Data'
In [2]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer

import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer

from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle

from tqdm import tqdm
import os

from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter
C:\Users\Anvesh\Anaconda3\lib\site-packages\gensim\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial
  warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")

1.1 Reading Data

In [3]:
from tqdm import tqdm
for i in tqdm(range(int(10e6))):
    pass
100%|█████████████████████████████████████████████████████████████████| 10000000/10000000 [00:03<00:00, 2719160.33it/s]
In [4]:
project_data = pd.read_csv('train_data.csv', nrows = 50000)
resource_data = pd.read_csv('resources.csv')
In [5]:
print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)
Number of data points in train data (50000, 17)
--------------------------------------------------
The attributes of data : ['Unnamed: 0' 'id' 'teacher_id' 'teacher_prefix' 'school_state'
 'project_submitted_datetime' 'project_grade_category'
 'project_subject_categories' 'project_subject_subcategories'
 'project_title' 'project_essay_1' 'project_essay_2' 'project_essay_3'
 'project_essay_4' 'project_resource_summary'
 'teacher_number_of_previously_posted_projects' 'project_is_approved']
In [6]:
print("Number of data points in train data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)
Number of data points in train data (1541272, 4)
['id' 'description' 'quantity' 'price']
Out[6]:
id description quantity price
0 p233245 LC652 - Lakeshore Double-Space Mobile Drying Rack 1 149.00
1 p069063 Bouncy Bands for Desks (Blue support pipes) 3 14.95

1.2 preprocessing of project_subject_categories

In [7]:
catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039

# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
    temp = ""
    # consider we have text like this "Math & Science, Warmth, Care & Hunger"
    for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
        temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
        temp = temp.replace('&','_') # we are replacing the & value into 
    cat_list.append(temp.strip())
    
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)

from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
    my_counter.update(word.split())

cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))

1.3 preprocessing of project_subject_subcategories

In [8]:
sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039

# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python

sub_cat_list = []
for i in sub_catogories:
    temp = ""
    # consider we have text like this "Math & Science, Warmth, Care & Hunger"
    for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
        temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
        temp = temp.replace('&','_')
    sub_cat_list.append(temp.strip())

project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)

# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
    my_counter.update(word.split())
    
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))

1.3 Text preprocessing

In [9]:
# merge two column text dataframe: 
project_data["essay"] = project_data["project_essay_1"].map(str) +\
                        project_data["project_essay_2"].map(str) + \
                        project_data["project_essay_3"].map(str) + \
                        project_data["project_essay_4"].map(str)
In [10]:
project_data.head(2)
Out[10]:
Unnamed: 0 id teacher_id teacher_prefix school_state project_submitted_datetime project_grade_category project_title project_essay_1 project_essay_2 project_essay_3 project_essay_4 project_resource_summary teacher_number_of_previously_posted_projects project_is_approved clean_categories clean_subcategories essay
0 160221 p253737 c90749f5d961ff158d4b4d1e7dc665fc Mrs. IN 2016-12-05 13:43:57 Grades PreK-2 Educational Support for English Learners at Home My students are English learners that are work... \"The limits of your language are the limits o... NaN NaN My students need opportunities to practice beg... 0 0 Literacy_Language ESL Literacy My students are English learners that are work...
1 140945 p258326 897464ce9ddc600bced1151f324dd63a Mr. FL 2016-10-25 09:22:10 Grades 6-8 Wanted: Projector for Hungry Learners Our students arrive to our school eager to lea... The projector we need for our school is very c... NaN NaN My students need a projector to help with view... 7 1 History_Civics Health_Sports Civics_Government TeamSports Our students arrive to our school eager to lea...
In [11]:
# printing some random reviews
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
print(project_data['essay'].values[20000])
print("="*50)
My students are English learners that are working on English as their second or third languages. We are a melting pot of refugees, immigrants, and native-born Americans bringing the gift of language to our school. \r\n\r\n We have over 24 languages represented in our English Learner program with students at every level of mastery.  We also have over 40 countries represented with the families within our school.  Each student brings a wealth of knowledge and experiences to us that open our eyes to new cultures, beliefs, and respect.\"The limits of your language are the limits of your world.\"-Ludwig Wittgenstein  Our English learner's have a strong support system at home that begs for more resources.  Many times our parents are learning to read and speak English along side of their children.  Sometimes this creates barriers for parents to be able to help their child learn phonetics, letter recognition, and other reading skills.\r\n\r\nBy providing these dvd's and players, students are able to continue their mastery of the English language even if no one at home is able to assist.  All families with students within the Level 1 proficiency status, will be a offered to be a part of this program.  These educational videos will be specially chosen by the English Learner Teacher and will be sent home regularly to watch.  The videos are to help the child develop early reading skills.\r\n\r\nParents that do not have access to a dvd player will have the opportunity to check out a dvd player to use for the year.  The plan is to use these videos and educational dvd's for the years to come for other EL students.\r\nnannan
==================================================
The 51 fifth grade students that will cycle through my classroom this year all love learning, at least most of the time. At our school, 97.3% of the students receive free or reduced price lunch. Of the 560 students, 97.3% are minority students. \r\nThe school has a vibrant community that loves to get together and celebrate. Around Halloween there is a whole school parade to show off the beautiful costumes that students wear. On Cinco de Mayo we put on a big festival with crafts made by the students, dances, and games. At the end of the year the school hosts a carnival to celebrate the hard work put in during the school year, with a dunk tank being the most popular activity.My students will use these five brightly colored Hokki stools in place of regular, stationary, 4-legged chairs. As I will only have a total of ten in the classroom and not enough for each student to have an individual one, they will be used in a variety of ways. During independent reading time they will be used as special chairs students will each use on occasion. I will utilize them in place of chairs at my small group tables during math and reading times. The rest of the day they will be used by the students who need the highest amount of movement in their life in order to stay focused on school.\r\n\r\nWhenever asked what the classroom is missing, my students always say more Hokki Stools. They can't get their fill of the 5 stools we already have. When the students are sitting in group with me on the Hokki Stools, they are always moving, but at the same time doing their work. Anytime the students get to pick where they can sit, the Hokki Stools are the first to be taken. There are always students who head over to the kidney table to get one of the stools who are disappointed as there are not enough of them. \r\n\r\nWe ask a lot of students to sit for 7 hours a day. The Hokki stools will be a compromise that allow my students to do desk work and move at the same time. These stools will help students to meet their 60 minutes a day of movement by allowing them to activate their core muscles for balance while they sit. For many of my students, these chairs will take away the barrier that exists in schools for a child who can't sit still.nannan
==================================================
How do you remember your days of school? Was it in a sterile environment with plain walls, rows of desks, and a teacher in front of the room? A typical day in our room is nothing like that. I work hard to create a warm inviting themed room for my students look forward to coming to each day.\r\n\r\nMy class is made up of 28 wonderfully unique boys and girls of mixed races in Arkansas.\r\nThey attend a Title I school, which means there is a high enough percentage of free and reduced-price lunch to qualify. Our school is an \"open classroom\" concept, which is very unique as there are no walls separating the classrooms. These 9 and 10 year-old students are very eager learners; they are like sponges, absorbing all the information and experiences and keep on wanting more.With these resources such as the comfy red throw pillows and the whimsical nautical hanging decor and the blue fish nets, I will be able to help create the mood in our classroom setting to be one of a themed nautical environment. Creating a classroom environment is very important in the success in each and every child's education. The nautical photo props will be used with each child as they step foot into our classroom for the first time on Meet the Teacher evening. I'll take pictures of each child with them, have them developed, and then hung in our classroom ready for their first day of 4th grade.  This kind gesture will set the tone before even the first day of school! The nautical thank you cards will be used throughout the year by the students as they create thank you cards to their team groups.\r\n\r\nYour generous donations will help me to help make our classroom a fun, inviting, learning environment from day one.\r\n\r\nIt costs lost of money out of my own pocket on resources to get our classroom ready. Please consider helping with this project to make our new school year a very successful one. Thank you!nannan
==================================================
My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations. \r\n\r\nThe materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills. \r\nThey also want to learn through games, my kids don't want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan
==================================================

Introducing New Features

Consider these set of features for Set 5 in Assignment:

categorical dataschool_state clean_categories....clean_subcategories....project_grade_category....teacher_prefix

numerical data quantity....teacher_number_of_previously_posted_projects....price

New Features

sentiment score's of each of the essay : numerical data
number of words in the title : numerical data
number of words in the combine essays : numerical data

In [12]:
import re
def decontracted(phrase):
    # specific
    phrase = re.sub(r"won't", "will not", phrase)
    phrase = re.sub(r"can\'t", "can not", phrase)

    # general
    phrase = re.sub(r"n\'t", " not", phrase)
    phrase = re.sub(r"\'re", " are", phrase)
    phrase = re.sub(r"\'s", " is", phrase)
    phrase = re.sub(r"\'d", " would", phrase)
    phrase = re.sub(r"\'ll", " will", phrase)
    phrase = re.sub(r"\'t", " not", phrase)
    phrase = re.sub(r"\'ve", " have", phrase)
    phrase = re.sub(r"\'m", " am", phrase)
    return phrase
In [13]:
new_title = []
for i in tqdm(project_data['project_title']):
    j = decontracted(i)
    new_title.append(j)   
    
100%|█████████████████████████████████████████████████████████████████████████| 50000/50000 [00:00<00:00, 74600.28it/s]
In [14]:
#Introducing New Features
title_word_count = []
#for i in project_data['project_title']:
for i in tqdm(new_title):
    j = len(i.split())
    title_word_count.append(j)
    #print(j)
project_data['title_word_count'] = title_word_count
100%|████████████████████████████████████████████████████████████████████████| 50000/50000 [00:00<00:00, 724730.28it/s]
In [15]:
project_data.head(2)
Out[15]:
Unnamed: 0 id teacher_id teacher_prefix school_state project_submitted_datetime project_grade_category project_title project_essay_1 project_essay_2 project_essay_3 project_essay_4 project_resource_summary teacher_number_of_previously_posted_projects project_is_approved clean_categories clean_subcategories essay title_word_count
0 160221 p253737 c90749f5d961ff158d4b4d1e7dc665fc Mrs. IN 2016-12-05 13:43:57 Grades PreK-2 Educational Support for English Learners at Home My students are English learners that are work... \"The limits of your language are the limits o... NaN NaN My students need opportunities to practice beg... 0 0 Literacy_Language ESL Literacy My students are English learners that are work... 7
1 140945 p258326 897464ce9ddc600bced1151f324dd63a Mr. FL 2016-10-25 09:22:10 Grades 6-8 Wanted: Projector for Hungry Learners Our students arrive to our school eager to lea... The projector we need for our school is very c... NaN NaN My students need a projector to help with view... 7 1 History_Civics Health_Sports Civics_Government TeamSports Our students arrive to our school eager to lea... 5
In [16]:
new_essay = []
for i in tqdm(project_data['essay']):
    j = decontracted(i)
    new_essay.append(j)  
100%|█████████████████████████████████████████████████████████████████████████| 50000/50000 [00:01<00:00, 41259.91it/s]
In [17]:
essay_word_count = []
for i in tqdm(new_essay):
    j = len(i.split())
    essay_word_count.append(j)
    #print(j)
project_data['essay_word_count'] = essay_word_count
100%|█████████████████████████████████████████████████████████████████████████| 50000/50000 [00:01<00:00, 41576.60it/s]
In [18]:
project_data.head(2)
Out[18]:
Unnamed: 0 id teacher_id teacher_prefix school_state project_submitted_datetime project_grade_category project_title project_essay_1 project_essay_2 project_essay_3 project_essay_4 project_resource_summary teacher_number_of_previously_posted_projects project_is_approved clean_categories clean_subcategories essay title_word_count essay_word_count
0 160221 p253737 c90749f5d961ff158d4b4d1e7dc665fc Mrs. IN 2016-12-05 13:43:57 Grades PreK-2 Educational Support for English Learners at Home My students are English learners that are work... \"The limits of your language are the limits o... NaN NaN My students need opportunities to practice beg... 0 0 Literacy_Language ESL Literacy My students are English learners that are work... 7 275
1 140945 p258326 897464ce9ddc600bced1151f324dd63a Mr. FL 2016-10-25 09:22:10 Grades 6-8 Wanted: Projector for Hungry Learners Our students arrive to our school eager to lea... The projector we need for our school is very c... NaN NaN My students need a projector to help with view... 7 1 History_Civics Health_Sports Civics_Government TeamSports Our students arrive to our school eager to lea... 5 221

Computing Sentiment Scores

In [19]:
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer

# import nltk
#nltk.download('vader_lexicon')

sid = SentimentIntensityAnalyzer()

for_sentiment = 'a person is a person no matter how small dr seuss i teach the smallest students with the biggest enthusiasm'
ss = sid.polarity_scores(for_sentiment)

for k in ss:
    print('{0}: {1}, '.format(k, ss[k]), end='')

# we can use these 4 things as features/attributes (neg, neu, pos, compound)
# neg: 0.0, neu: 0.753, pos: 0.247, compound: 0.93
neg: 0.109, neu: 0.693, pos: 0.198, compound: 0.2023, 
In [20]:
SID = SentimentIntensityAnalyzer()
#There is NEGITIVE and POSITIVE and NEUTRAL and COMPUND SCORES
#http://www.nltk.org/howto/sentiment.html
    
negitive = []
positive = []
neutral = []
compound = []
for i in tqdm(project_data['essay']):
    j = SID.polarity_scores(i)['neg']
    k = SID.polarity_scores(i)['neu']
    l = SID.polarity_scores(i)['pos']
    m = SID.polarity_scores(i)['compound']
    negitive.append(j)
    positive.append(k)
    neutral.append(l)
    compound.append(m)
100%|████████████████████████████████████████████████████████████████████████████| 50000/50000 [12:57<00:00, 64.27it/s]
In [21]:
project_data['negitive'] = negitive
project_data['positive'] = positive
project_data['neutral'] = neutral
project_data['compound'] = compound
In [22]:
project_data.head(2)
Out[22]:
Unnamed: 0 id teacher_id teacher_prefix school_state project_submitted_datetime project_grade_category project_title project_essay_1 project_essay_2 ... project_is_approved clean_categories clean_subcategories essay title_word_count essay_word_count negitive positive neutral compound
0 160221 p253737 c90749f5d961ff158d4b4d1e7dc665fc Mrs. IN 2016-12-05 13:43:57 Grades PreK-2 Educational Support for English Learners at Home My students are English learners that are work... \"The limits of your language are the limits o... ... 0 Literacy_Language ESL Literacy My students are English learners that are work... 7 275 0.008 0.911 0.081 0.9611
1 140945 p258326 897464ce9ddc600bced1151f324dd63a Mr. FL 2016-10-25 09:22:10 Grades 6-8 Wanted: Projector for Hungry Learners Our students arrive to our school eager to lea... The projector we need for our school is very c... ... 1 History_Civics Health_Sports Civics_Government TeamSports Our students arrive to our school eager to lea... 5 221 0.037 0.851 0.112 0.9267

2 rows × 24 columns

1.4 Train_Test_Split

In [23]:
#Train Test Split 
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(project_data, project_data["project_is_approved"],
                                                    test_size = 0.33, stratify = project_data["project_is_approved"],
                                                    random_state = 42)
In [24]:
#Train CV Split
x_train, x_cv, y_train, y_cv = train_test_split(x_train, y_train, test_size = 0.33, stratify = y_train,
                                                random_state = 42)
In [25]:
print(x_test.columns)
print(x_train.columns)
#print(x_cv.columns)
#print(x_train.shape)
#print(x_test.shape)
#print(x_cv.shape)
Index(['Unnamed: 0', 'id', 'teacher_id', 'teacher_prefix', 'school_state',
       'project_submitted_datetime', 'project_grade_category', 'project_title',
       'project_essay_1', 'project_essay_2', 'project_essay_3',
       'project_essay_4', 'project_resource_summary',
       'teacher_number_of_previously_posted_projects', 'project_is_approved',
       'clean_categories', 'clean_subcategories', 'essay', 'title_word_count',
       'essay_word_count', 'negitive', 'positive', 'neutral', 'compound'],
      dtype='object')
Index(['Unnamed: 0', 'id', 'teacher_id', 'teacher_prefix', 'school_state',
       'project_submitted_datetime', 'project_grade_category', 'project_title',
       'project_essay_1', 'project_essay_2', 'project_essay_3',
       'project_essay_4', 'project_resource_summary',
       'teacher_number_of_previously_posted_projects', 'project_is_approved',
       'clean_categories', 'clean_subcategories', 'essay', 'title_word_count',
       'essay_word_count', 'negitive', 'positive', 'neutral', 'compound'],
      dtype='object')
In [26]:
#Dropping Class Label in train test and cv data
x_train.drop(["project_is_approved"], axis = 1, inplace = True)
x_test.drop(["project_is_approved"], axis = 1, inplace = True)
x_cv.drop(["project_is_approved"], axis = 1, inplace = True)
In [27]:
print(x_train.columns)
Index(['Unnamed: 0', 'id', 'teacher_id', 'teacher_prefix', 'school_state',
       'project_submitted_datetime', 'project_grade_category', 'project_title',
       'project_essay_1', 'project_essay_2', 'project_essay_3',
       'project_essay_4', 'project_resource_summary',
       'teacher_number_of_previously_posted_projects', 'clean_categories',
       'clean_subcategories', 'essay', 'title_word_count', 'essay_word_count',
       'negitive', 'positive', 'neutral', 'compound'],
      dtype='object')
In [ ]:
 

Preparing Data for Models

In [28]:
# https://stackoverflow.com/a/47091490/4084039
import re

def decontracted(phrase):
    # specific
    phrase = re.sub(r"won't", "will not", phrase)
    phrase = re.sub(r"can\'t", "can not", phrase)

    # general
    phrase = re.sub(r"n\'t", " not", phrase)
    phrase = re.sub(r"\'re", " are", phrase)
    phrase = re.sub(r"\'s", " is", phrase)
    phrase = re.sub(r"\'d", " would", phrase)
    phrase = re.sub(r"\'ll", " will", phrase)
    phrase = re.sub(r"\'t", " not", phrase)
    phrase = re.sub(r"\'ve", " have", phrase)
    phrase = re.sub(r"\'m", " am", phrase)
    return phrase
In [29]:
sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)
My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations. \r\n\r\nThe materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills. \r\nThey also want to learn through games, my kids do not want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan
==================================================
In [30]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)
My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations.     The materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills.   They also want to learn through games, my kids do not want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan
In [31]:
#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)
My kindergarten students have varied disabilities ranging from speech and language delays cognitive delays gross fine motor delays to autism They are eager beavers and always strive to work their hardest working past their limitations The materials we have are the ones I seek out for my students I teach in a Title I school where most of the students receive free or reduced price lunch Despite their disabilities and limitations my students love coming to school and come eager to learn and explore Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting This is how my kids feel all the time The want to be able to move as they learn or so they say Wobble chairs are the answer and I love then because they develop their core which enhances gross motor and in Turn fine motor skills They also want to learn through games my kids do not want to sit and do worksheets They want to learn to count by jumping and playing Physical engagement is the key to our success The number toss and color and shape mats can make that happen My students will forget they are doing work and just have the fun a 6 year old deserves nannan
In [32]:
# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
            "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
            'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
            's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
            've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
            "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
            "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
            'won', "won't", 'wouldn', "wouldn't"]

Project_Essays preprocessing

In [33]:
#train_preprocessed_essays 
# Combining all the above stundents 
from tqdm import tqdm
train_preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(x_train['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    train_preprocessed_essays.append(sent.lower().strip())
100%|██████████████████████████████████████████████████████████████████████████| 22445/22445 [00:17<00:00, 1262.92it/s]
In [34]:
train_preprocessed_essays[10]
Out[34]:
'my students naturally inquisitive respectful innate concern others community well outside community 90 love read fun exploring new genres within classroom they connect thematic elements text outside world oftentimes bring new item piece information pertaining unit time they brilliant give hope future area state country they running place another 20 30 years vital nurture naturally inquisitive minds fullest extent possible help student learning skyrocket tools engage formative assessment students use markers erasers individual lapboards provide feedback answers questions posed reviewed content they also use review games planning group collaboration inferences within text reading the teacher complete view grasped material not the mobile organizer used organize markers erasers pencils glue classwork better organization ensure class runs smoothly kids teacher stay focused instruction time higher quality nannan'
In [35]:
# test_preprocessed_essay
from tqdm import tqdm
test_preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(x_test['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    test_preprocessed_essays.append(sent.lower().strip())
100%|██████████████████████████████████████████████████████████████████████████| 16500/16500 [00:13<00:00, 1262.76it/s]
In [36]:
test_preprocessed_essays[10]
Out[36]:
'our elementary school beautifully old building 91 years old houses many classrooms ranging kindergarten fifth grade it home full adults children love learn new students joining family year kindergarteners the students enter classroom year excited nervous unaware fun magic awaits they start academic journey many entering school first time my students come backgrounds deserving love endless opportunites they love discovering ways guided encouraged become independent take ownership learning studies show music vital part learning certain areas brain activated music help many different types learners classroom my students feel inspired songs i carefully choose match theme they learn active means healthy happy dance fun beats favorite songs they not help sing dance along lighting fire learning there many different types learners good majority find music great way help remember things somewhat tedious parts day clean become exciting tidy beat music slow fast kindergarten also creating things whether tea party set ready serve dramatic play giant cave shivering bear construciton area students love take pictures work proudly show classmates family pictures spark ideas ideas motivate young authors write stories this camera help time travel back small moments learning write the possiblities simply endless nannan'
In [37]:
# CV_preprocessed_essays
from tqdm import tqdm
cv_preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(x_cv['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    cv_preprocessed_essays.append(sent.lower().strip())
100%|██████████████████████████████████████████████████████████████████████████| 11055/11055 [00:09<00:00, 1143.86it/s]
In [38]:
cv_preprocessed_essays[10]
Out[38]:
'our students great time using bright colorful glazes expensive not afford please help us get basic colors finish project help us purchase tools create beautiful works art for many first experience fine arts clay this high school mix cultures socioeconomic backgrounds some students not speak much english still create clay projects we also students learning disabilities ceramics classes our school currently california distinguished school teachers students proud hard work this one first projects students make clay they introduced coil building technique well introduced making repeated patterns clay shapes the students create original pot shows unique personality ideas they learn many artists art elements principles well learn basic ceramic building techniques they influenced anything around pop culture artists even disney movies influences lives this project use common core state visual arts standards then also paint patterned pots bright vivid glaze colors make designs choose stand look complete my students need coil extruder tool help create coils patterned pots this project help students learn think creatively help develop new ideas different anyone else around they also appreciate arts learn create designs fun unique the students really use equipment complete projects not normally afford high cost materials nannan'

Project_Titles_preprocessing

In [39]:
# train_preprocessed_title
from tqdm import tqdm
train_preprocessed_titles = []
# tqdm is for printing the status bar
for sentance in tqdm(x_train['project_title'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    train_preprocessed_titles.append(sent.lower().strip())
100%|█████████████████████████████████████████████████████████████████████████| 22445/22445 [00:00<00:00, 26698.55it/s]
In [40]:
train_preprocessed_titles[10]
Out[40]:
'show me work student lap board markers organizer'
In [41]:
# Test_preprocessed_essays
from tqdm import tqdm
test_preprocessed_titles = []
# tqdm is for printing the status bar
for sentance in tqdm(x_test['project_title'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    test_preprocessed_titles.append(sent.lower().strip())
100%|█████████████████████████████████████████████████████████████████████████| 16500/16500 [00:00<00:00, 26303.42it/s]
In [42]:
test_preprocessed_titles[10]
Out[42]:
'we need make music capture memories'
In [43]:
# CV_preprocessed_titles
from tqdm import tqdm
cv_preprocessed_titles = []
# tqdm is for printing the status bar
for sentance in tqdm(x_cv['project_title'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e not in stopwords)
    cv_preprocessed_titles.append(sent.lower().strip())
100%|█████████████████████████████████████████████████████████████████████████| 11055/11055 [00:00<00:00, 24759.71it/s]
In [44]:
# after preprocesing
cv_preprocessed_titles[10]
Out[44]:
'creative coil pottery'
In [45]:
project_data.columns
Out[45]:
Index(['Unnamed: 0', 'id', 'teacher_id', 'teacher_prefix', 'school_state',
       'project_submitted_datetime', 'project_grade_category', 'project_title',
       'project_essay_1', 'project_essay_2', 'project_essay_3',
       'project_essay_4', 'project_resource_summary',
       'teacher_number_of_previously_posted_projects', 'project_is_approved',
       'clean_categories', 'clean_subcategories', 'essay', 'title_word_count',
       'essay_word_count', 'negitive', 'positive', 'neutral', 'compound'],
      dtype='object')

we are going to consider

   - school_state : categorical data
   - clean_categories : categorical data
   - clean_subcategories : categorical data
   - project_grade_category : categorical data
   - teacher_prefix : categorical data

   - project_title : text data
   - text : text data
   - project_resource_summary: text data (optinal)

   - quantity : numerical (optinal)
   - teacher_number_of_previously_posted_projects : numerical
   - price : numerical

1.5.1 Vectorizing Categorical data

In [46]:
# we use count vectorizer to convert the values into one 
# Vectorizing Clean Categories 
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)

vectorizer.fit(x_train['clean_categories'].values)
train_categories_one_hot = vectorizer.fit_transform(x_train['clean_categories'].values)
test_categories_one_hot = vectorizer.transform(x_test['clean_categories'].values)
cv_categories_one_hot = vectorizer.transform(x_cv['clean_categories'].values)

cat_feat = vectorizer.get_feature_names()   #For Decision Tree
print(cat_feat)
print("Shape of  Train matrix after one hot encodig ",train_categories_one_hot.shape)
print("Shape of  Test matrix after one hot encodig ",test_categories_one_hot.shape)
print("Shape of  cv  matrix after one hot encodig ",cv_categories_one_hot.shape)
['Warmth', 'Care_Hunger', 'History_Civics', 'Music_Arts', 'AppliedLearning', 'SpecialNeeds', 'Health_Sports', 'Math_Science', 'Literacy_Language']
Shape of  Train matrix after one hot encodig  (22445, 9)
Shape of  Test matrix after one hot encodig  (16500, 9)
Shape of  cv  matrix after one hot encodig  (11055, 9)
In [47]:
# we use count vectorizer to convert the values into one 
vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(x_train["clean_subcategories"].values)
train_sub_categories_one_hot = vectorizer.fit_transform(x_train['clean_subcategories'].values)
test_sub_categories_one_hot = vectorizer.transform(x_test['clean_subcategories'].values)
cv_sub_categories_one_hot = vectorizer.transform(x_cv['clean_subcategories'].values)

sub_cat_feat = vectorizer.get_feature_names() #For Decision Tree

print(vectorizer.get_feature_names())
print("Shape of Train matrix after  Trainone hot encodig ",train_sub_categories_one_hot.shape)
print("Shape of test matrix after one hot encodig ",test_sub_categories_one_hot.shape)
print("Shape of cv_ matrix after  one hot encodig ",cv_sub_categories_one_hot.shape)
['Economics', 'CommunityService', 'FinancialLiteracy', 'ParentInvolvement', 'Extracurricular', 'Civics_Government', 'ForeignLanguages', 'NutritionEducation', 'Warmth', 'Care_Hunger', 'SocialSciences', 'PerformingArts', 'CharacterEducation', 'TeamSports', 'Other', 'College_CareerPrep', 'Music', 'History_Geography', 'Health_LifeScience', 'EarlyDevelopment', 'ESL', 'Gym_Fitness', 'EnvironmentalScience', 'VisualArts', 'Health_Wellness', 'AppliedSciences', 'SpecialNeeds', 'Literature_Writing', 'Mathematics', 'Literacy']
Shape of Train matrix after  Trainone hot encodig  (22445, 30)
Shape of test matrix after one hot encodig  (16500, 30)
Shape of cv_ matrix after  one hot encodig  (11055, 30)
In [48]:
# you can do the similar thing with state, teacher_prefix and project_grade_category also
from collections import Counter
my_counter = Counter()
for word in project_data["school_state"].values:
    my_counter.update(word.split())
In [49]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
state_cat_dict = dict(my_counter)
storted_state_cat_dict = dict(sorted(state_cat_dict.items(), key=lambda kv: kv[1]))
In [50]:
# Please do the similar feature encoding with state, teacher_prefix and project_grade_category also
#Using Count Vectorizer to convert the state value onto on hot encoded feature
vectorizer = CountVectorizer(vocabulary=list(storted_state_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['school_state'].values)
print(vectorizer.get_feature_names())

school_feat = vectorizer.get_feature_names() #For Decision Tree

train_state_one_hot = vectorizer.fit_transform(x_train['school_state'].values)
test_state_one_hot = vectorizer.transform(x_test['school_state'].values)
cv_state_one_hot = vectorizer.transform(x_cv['school_state'].values)

print("Shape of Train matrix after one hot encodig ",train_state_one_hot.shape)
print("Shape of Test matrix after one hot encodig ",test_state_one_hot.shape)
print("Shape of CV matrix after one hot encodig ",cv_state_one_hot.shape)
['VT', 'WY', 'ND', 'MT', 'RI', 'NH', 'SD', 'NE', 'AK', 'DE', 'WV', 'ME', 'NM', 'HI', 'DC', 'KS', 'ID', 'IA', 'AR', 'CO', 'MN', 'OR', 'MS', 'KY', 'NV', 'MD', 'CT', 'TN', 'AL', 'UT', 'WI', 'VA', 'AZ', 'NJ', 'OK', 'MA', 'LA', 'WA', 'MO', 'IN', 'OH', 'PA', 'MI', 'GA', 'SC', 'IL', 'NC', 'FL', 'TX', 'NY', 'CA']
Shape of Train matrix after one hot encodig  (22445, 51)
Shape of Test matrix after one hot encodig  (16500, 51)
Shape of CV matrix after one hot encodig  (11055, 51)
In [51]:
#https://stackoverflow.com/questions/42224700/attributeerror-float-object-has-no-attribute-split
#project_data['project_grade_category']=project_data['project_grade_category'].fillna("")
my_counter = Counter()
for word in project_data['project_grade_category'].values:
    my_counter.update(word.split())
In [52]:
project_cat_dict = dict(my_counter)
sorted_project_cat_dict = dict(sorted(project_cat_dict.items(), key=lambda kv: kv[1]))
In [53]:
# feature encoding for project_grade_category also
vectorizer = CountVectorizer(vocabulary=list(sorted_project_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['project_grade_category'].values)
print(vectorizer.get_feature_names())

grade_feat = vectorizer.get_feature_names() #For Decision Tree

train_grade_one_hot = vectorizer.fit_transform(x_train['project_grade_category'].values)
test_grade_one_hot = vectorizer.transform(x_test['project_grade_category'].values)
cv_grade_one_hot = vectorizer.transform(x_cv['project_grade_category'].values)

train_grade_one_hot = train_grade_one_hot[:, 0:4]
test_grade_one_hot = test_grade_one_hot[:, 0:4]
cv_grade_one_hot = cv_grade_one_hot[:, 0:4]

print("Shape of Train matrix after one hot encodig ",train_grade_one_hot.shape)
print("Shape of test matrix after one hot encodig ",test_grade_one_hot.shape)
print("Shape of cv matrix after one hot encodig ",cv_grade_one_hot.shape)
['9-12', '6-8', '3-5', 'PreK-2', 'Grades']
Shape of Train matrix after one hot encodig  (22445, 4)
Shape of test matrix after one hot encodig  (16500, 4)
Shape of cv matrix after one hot encodig  (11055, 4)
In [54]:
#https://stackoverflow.com/questions/42224700/attributeerror-float-object-has-no-attribute-split
project_data['teacher_prefix']=project_data['teacher_prefix'].fillna(" ")
In [55]:
my_counter = Counter()
for word in project_data['teacher_prefix'].values:
    my_counter.update(word.split())
In [56]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
teacher_cat_dict = dict(my_counter)
sorted_teacher_cat_dict = dict(sorted(teacher_cat_dict.items(), key=lambda kv: kv[1]))
In [57]:
#Using Count Vectorizer to convert the teacher_prefix value onto on hot encoded feature
#ValueError: np.nan is an invalid document, expected byte or unicode string.
#https://stackoverflow.com/questions/39303912/tfidfvectorizer-in-scikit-learn-valueerror-np-nan-is-an-invalid-document
vectorizer = CountVectorizer(vocabulary=list(sorted_teacher_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['teacher_prefix'].values.astype('U'))
print(vectorizer.get_feature_names())

teacher_feat = vectorizer.get_feature_names() #For Decision Tree

train_teacher_one_hot = vectorizer.fit_transform(x_train['teacher_prefix'].values.astype('U'))
test_teacher_one_hot = vectorizer.transform(x_test['teacher_prefix'].values.astype('U'))
cv_teacher_one_hot = vectorizer.transform(x_cv['teacher_prefix'].values.astype('U'))

print("Shape of Train matrix after one hot encodig ",train_teacher_one_hot.shape)
print("Shape of Test matrix after one hot encodig ",test_teacher_one_hot.shape)
print("Shape of CV matrix after one hot encodig ",cv_teacher_one_hot.shape)
['Dr.', 'Teacher', 'Mr.', 'Ms.', 'Mrs.']
Shape of Train matrix after one hot encodig  (22445, 5)
Shape of Test matrix after one hot encodig  (16500, 5)
Shape of CV matrix after one hot encodig  (11055, 5)

1.5.2 Vectorizing Text data

1.5.2.1 Bag of words

Minimum frequency of words 10 (min_df = 10) of ALL features

In [58]:
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
vectorizer.fit(train_preprocessed_essays)

BOW_feat_essay = vectorizer.get_feature_names() #For Decision Tree

train_text_bow = vectorizer.fit_transform(train_preprocessed_essays)
print("Shape of matrix after one hot encodig ",train_text_bow.shape)
Shape of matrix after one hot encodig  (22445, 8869)
In [59]:
#Vectorizing Test Data
# you can vectorize the title also 
# before you vectorize the title make sure you preprocess it
test_text_bow = vectorizer.transform(test_preprocessed_essays)
print("Shape of matrix after one hot encodig ",test_text_bow.shape)
Shape of matrix after one hot encodig  (16500, 8869)
In [60]:
# Vectrozing CV Data 
cv_text_bow = vectorizer.transform(cv_preprocessed_essays)
print("Shape of matrix after one hot encodig ",cv_text_bow.shape)
Shape of matrix after one hot encodig  (11055, 8869)

Project_Title BOW

In [61]:
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
vectorizer.fit(train_preprocessed_titles)

BOW_feat_title = vectorizer.get_feature_names()

train_titles_bow = vectorizer.fit_transform(train_preprocessed_titles)
print("Shape of matrix after one hot encodig ",train_titles_bow.shape)
Shape of matrix after one hot encodig  (22445, 1229)
In [62]:
#Vectorizing Test Data
test_titles_bow = vectorizer.transform(test_preprocessed_titles)
print("Shape of matrix after one hot encodig ",test_titles_bow.shape)
Shape of matrix after one hot encodig  (16500, 1229)
In [63]:
#Vectrizing CV Data
cv_titles_bow = vectorizer.transform(cv_preprocessed_titles)
print("Shape of matrix after one hot encodig ",cv_titles_bow.shape)
Shape of matrix after one hot encodig  (11055, 1229)

1.5.2.2 TFIDF vectorizer

Here also we are considering word frequency of 10 words(min_df = 10) of all features

In [64]:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
vectorizer.fit(train_preprocessed_essays)

TFIDF_feat_essay = vectorizer.get_feature_names()

train_text_tfidf = vectorizer.fit_transform(train_preprocessed_essays)
print("Shape of matrix after one hot encodig ",train_text_tfidf.shape)
Shape of matrix after one hot encodig  (22445, 8869)
In [65]:
test_text_tfidf = vectorizer.transform(test_preprocessed_essays)
print("Shape of matrix after one hot encodig ",test_text_tfidf.shape)
Shape of matrix after one hot encodig  (16500, 8869)
In [66]:
cv_text_tfidf = vectorizer.transform(cv_preprocessed_essays)
print("Shape of matrix after one hot encodig ",cv_text_tfidf.shape)
Shape of matrix after one hot encodig  (11055, 8869)

Project Titles

In [67]:
vectorizer = TfidfVectorizer(min_df=10)
vectorizer.fit(train_preprocessed_titles)

TFIDF_feat_titles = vectorizer.get_feature_names()

train_title_tfidf = vectorizer.fit_transform(train_preprocessed_titles)
print("Shape of matrix after one hot encodig ",train_title_tfidf.shape)
Shape of matrix after one hot encodig  (22445, 1229)
In [68]:
test_title_tfidf = vectorizer.transform(test_preprocessed_titles)
print("Shape of matrix after one hot encodig ",test_title_tfidf.shape)
Shape of matrix after one hot encodig  (16500, 1229)
In [69]:
cv_title_tfidf = vectorizer.transform(cv_preprocessed_titles)
print("Shape of matrix after one hot encodig ",cv_title_tfidf.shape)
Shape of matrix after one hot encodig  (11055, 1229)

1.5.2.3 Using Pretrained Models: Avg W2V

In [70]:
'''
# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
def loadGloveModel(gloveFile):
    print ("Loading Glove Model")
    f = open(gloveFile,'r', encoding="utf8")
    model = {}
    for line in tqdm(f):
        splitLine = line.split()
        word = splitLine[0]
        embedding = np.array([float(val) for val in splitLine[1:]])
        model[word] = embedding
    print ("Done.",len(model)," words loaded!")
    return model
model = loadGloveModel('glove.42B.300d.txt')

# ============================
Output:
    
Loading Glove Model
1917495it [06:32, 4879.69it/s]
Done. 1917495  words loaded!

# ============================

words = []
for i in preproced_texts:
    words.extend(i.split(' '))

for i in preproced_titles:
    words.extend(i.split(' '))
print("all the words in the coupus", len(words))
words = set(words)
print("the unique words in the coupus", len(words))

inter_words = set(model.keys()).intersection(words)
print("The number of words that are present in both glove vectors and our coupus", \
      len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")

words_courpus = {}
words_glove = set(model.keys())
for i in words:
    if i in words_glove:
        words_courpus[i] = model[i]
print("word 2 vec length", len(words_courpus))


# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/

import pickle
with open('glove_vectors', 'wb') as f:
    pickle.dump(words_courpus, f)


'''
Out[70]:
'\n# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039\ndef loadGloveModel(gloveFile):\n    print ("Loading Glove Model")\n    f = open(gloveFile,\'r\', encoding="utf8")\n    model = {}\n    for line in tqdm(f):\n        splitLine = line.split()\n        word = splitLine[0]\n        embedding = np.array([float(val) for val in splitLine[1:]])\n        model[word] = embedding\n    print ("Done.",len(model)," words loaded!")\n    return model\nmodel = loadGloveModel(\'glove.42B.300d.txt\')\n\n# ============================\nOutput:\n    \nLoading Glove Model\n1917495it [06:32, 4879.69it/s]\nDone. 1917495  words loaded!\n\n# ============================\n\nwords = []\nfor i in preproced_texts:\n    words.extend(i.split(\' \'))\n\nfor i in preproced_titles:\n    words.extend(i.split(\' \'))\nprint("all the words in the coupus", len(words))\nwords = set(words)\nprint("the unique words in the coupus", len(words))\n\ninter_words = set(model.keys()).intersection(words)\nprint("The number of words that are present in both glove vectors and our coupus",       len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")\n\nwords_courpus = {}\nwords_glove = set(model.keys())\nfor i in words:\n    if i in words_glove:\n        words_courpus[i] = model[i]\nprint("word 2 vec length", len(words_courpus))\n\n\n# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/\n\nimport pickle\nwith open(\'glove_vectors\', \'wb\') as f:\n    pickle.dump(words_courpus, f)\n\n\n'
In [71]:
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
with open('glove_vectors', 'rb') as f:
    model = pickle.load(f)
    glove_words =  set(model.keys())
In [72]:
# average Word2Vec
# compute average word2vec for each review.
train_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(train_preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    train_avg_w2v_vectors.append(vector)

print(len(train_avg_w2v_vectors))
print(len(train_avg_w2v_vectors[0]))
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22445
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In [73]:
# average Word2Vec
# compute average word2vec for each review.
test_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(test_preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    test_avg_w2v_vectors.append(vector)

print(len(test_avg_w2v_vectors))
print(len(test_avg_w2v_vectors[0]))
100%|██████████████████████████████████████████████████████████████████████████| 16500/16500 [00:07<00:00, 2219.44it/s]
16500
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In [74]:
# average Word2Vec
# compute average word2vec for each review.
cv_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(cv_preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    cv_avg_w2v_vectors.append(vector)

print(len(cv_avg_w2v_vectors))
print(len(cv_avg_w2v_vectors[0]))
100%|██████████████████████████████████████████████████████████████████████████| 11055/11055 [00:04<00:00, 2251.68it/s]
11055
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AVG_W2V Project_Titles

In [75]:
# average Word2Vec
# compute average word2vec for each review.
train_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(train_preprocessed_titles): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    train_title_avg_w2v_vectors.append(vector)

print(len(train_title_avg_w2v_vectors))
print(len(train_title_avg_w2v_vectors[0]))
100%|█████████████████████████████████████████████████████████████████████████| 22445/22445 [00:00<00:00, 37899.73it/s]
22445
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In [76]:
# average Word2Vec
# compute average word2vec for each review.
test_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(test_preprocessed_titles): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    test_title_avg_w2v_vectors.append(vector)

print(len(test_title_avg_w2v_vectors))
print(len(test_title_avg_w2v_vectors[0]))
100%|█████████████████████████████████████████████████████████████████████████| 16500/16500 [00:00<00:00, 36776.40it/s]
16500
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In [77]:
# average Word2Vec
# compute average word2vec for each review.
cv_title_avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(cv_preprocessed_titles): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    cv_title_avg_w2v_vectors.append(vector)

print(len(cv_title_avg_w2v_vectors))
print(len(cv_title_avg_w2v_vectors[0]))
100%|█████████████████████████████████████████████████████████████████████████| 11055/11055 [00:00<00:00, 38769.71it/s]
11055
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1.5.2.3 Using Pretrained Models: TFIDF weighted W2V

In [78]:
# S = ["abc def pqr", "def def def abc", "pqr pqr def"]
tfidf_model = TfidfVectorizer()
tfidf_model.fit(train_preprocessed_essays)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [79]:
# average Word2Vec
# compute average word2vec for each review.
train_essay_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(train_preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    train_essay_tfidf_w2v_vectors.append(vector)

print(len(train_essay_tfidf_w2v_vectors))
print(len(train_essay_tfidf_w2v_vectors[0]))
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22445
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In [80]:
# Similarly you can vectorize for title also
# average Word2Vec
# compute average word2vec for each review.
test_essay_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(test_preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    test_essay_tfidf_w2v_vectors.append(vector)

print(len(test_essay_tfidf_w2v_vectors))
print(len(test_essay_tfidf_w2v_vectors[0]))
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16500
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In [81]:
# average Word2Vec
# compute average word2vec for each review.
cv_essay_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(cv_preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    cv_essay_tfidf_w2v_vectors.append(vector)

print(len(cv_essay_tfidf_w2v_vectors))
print(len(cv_essay_tfidf_w2v_vectors[0]))
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Proect_titles

In [82]:
# average Word2Vec
# compute average word2vec for each review.
train_title_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(train_preprocessed_titles): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    train_title_tfidf_w2v_vectors.append(vector)

print(len(train_title_tfidf_w2v_vectors))
print(len(train_title_tfidf_w2v_vectors[0]))
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In [83]:
# average Word2Vec
# compute average word2vec for each review.
test_title_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(test_preprocessed_titles): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    test_title_tfidf_w2v_vectors.append(vector)

print(len(test_title_tfidf_w2v_vectors))
print(len(test_title_tfidf_w2v_vectors[0]))
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In [84]:
# average Word2Vec
# compute average word2vec for each review.
cv_title_tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(cv_preprocessed_titles): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    cv_title_tfidf_w2v_vectors.append(vector)

print(len(cv_title_tfidf_w2v_vectors))
print(len(cv_title_tfidf_w2v_vectors[0]))
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1.5.3 Vectorizing Numerical features

In [85]:
price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
project_data = pd.merge(project_data, price_data, on='id', how='left')
print(price_data.head())
#print(project_data.columns)
print(x_train.columns)
        id    price  quantity
0  p000001   459.56         7
1  p000002   515.89        21
2  p000003   298.97         4
3  p000004  1113.69        98
4  p000005   485.99         8
Index(['Unnamed: 0', 'id', 'teacher_id', 'teacher_prefix', 'school_state',
       'project_submitted_datetime', 'project_grade_category', 'project_title',
       'project_essay_1', 'project_essay_2', 'project_essay_3',
       'project_essay_4', 'project_resource_summary',
       'teacher_number_of_previously_posted_projects', 'clean_categories',
       'clean_subcategories', 'essay', 'title_word_count', 'essay_word_count',
       'negitive', 'positive', 'neutral', 'compound'],
      dtype='object')
In [86]:
#   - quantity : numerical (optinal)
#   - teacher_number_of_previously_posted_projects : numerical
#   - price : numerical
x_train = pd.merge(x_train, price_data, on = "id", how = "left")
#print(x_train.columns)
x_test = pd.merge(x_test, price_data, on = "id", how = "left")
x_cv = pd.merge(x_cv, price_data, on = "id", how = "left")

Standardize Price

In [87]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler

# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329.   ... 399.   287.73   5.5 ].
# Reshape your data either using array.reshape(-1, 1)

price_scalar = StandardScaler()

price_scalar.fit(x_train['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TRAIN -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_price_standar = price_scalar.transform(x_train['price'].values.reshape(-1, 1))

price_scalar.fit(x_test['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TEST -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_price_standar = price_scalar.transform(x_test['price'].values.reshape(-1, 1))

price_scalar.fit(x_cv['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"CV -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
cv_price_standar = price_scalar.transform(x_cv['price'].values.reshape(-1, 1))
TRAIN -> Mean : 297.71562753397194, Standard deviation : 373.5890845438879
TEST -> Mean : 301.40470666666664, Standard deviation : 387.6948484063583
CV -> Mean : 299.52771506105836, Standard deviation : 373.1478347400019
In [88]:
print(train_price_standar.shape, y_train.shape)
print(test_price_standar.shape, y_test.shape)
print(cv_price_standar.shape, y_cv.shape)
(22445, 1) (22445,)
(16500, 1) (16500,)
(11055, 1) (11055,)

Standardize Teacher previously posted Projects

In [89]:
warnings.filterwarnings("ignore")
price_scalar.fit(x_train['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TRAIN -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_prev_proj_standar = price_scalar.transform(x_train['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))

price_scalar.fit(x_test['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TEST -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_prev_proj_standar = price_scalar.transform(x_test['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))

price_scalar.fit(x_cv['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"CV -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
cv_prev_proj_standar = price_scalar.transform(x_cv['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))
TRAIN -> Mean : 11.66473602138561, Standard deviation : 29.394684894505467
TEST -> Mean : 10.824363636363636, Standard deviation : 26.94848396907338
CV -> Mean : 11.040253279059248, Standard deviation : 27.32928192601594
In [90]:
print(train_prev_proj_standar.shape, y_train.shape)
print(test_prev_proj_standar.shape, y_test.shape)
print(cv_prev_proj_standar.shape, y_cv.shape)
(22445, 1) (22445,)
(16500, 1) (16500,)
(11055, 1) (11055,)

Standardize Quantity

In [91]:
price_scalar.fit(x_train['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TRAIN -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
train_quantity_standar = price_scalar.transform(x_train['quantity'].values.reshape(-1, 1))

price_scalar.fit(x_test['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"TEST -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
test_quantity_standar = price_scalar.transform(x_test['quantity'].values.reshape(-1, 1))

price_scalar.fit(x_cv['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"CV -> Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
cv_quantity_standar = price_scalar.transform(x_cv['quantity'].values.reshape(-1, 1))
TRAIN -> Mean : 17.001470260637113, Standard deviation : 26.03701886058425
TEST -> Mean : 17.05290909090909, Standard deviation : 27.494020077501187
CV -> Mean : 17.173224785165083, Standard deviation : 27.30705061932881
In [92]:
print(train_quantity_standar.shape, y_train.shape)
print(test_quantity_standar.shape, y_test.shape)
print(cv_quantity_standar.shape, y_cv.shape)
(22445, 1) (22445,)
(16500, 1) (16500,)
(11055, 1) (11055,)

Standardize Title_word_count

In [93]:
title_scalar = StandardScaler()
title_scalar.fit(x_train['title_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {title_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
train_title_word_count_standar = title_scalar.transform(x_train['title_word_count'].values.reshape(-1, 1))

title_scalar.fit(x_test['title_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {title_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
test_title_word_count_standar = title_scalar.transform(x_test['title_word_count'].values.reshape(-1, 1))

title_scalar.fit(x_cv['title_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {title_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
cv_title_word_count_standar = title_scalar.transform(x_cv['quantity'].values.reshape(-1, 1))

print(train_title_word_count_standar.shape, y_train.shape)
print(test_title_word_count_standar.shape, y_test.shape)
print(cv_title_word_count_standar.shape, y_cv.shape)
Mean : 5.215103586544887, Standard deviation : 27.30705061932881
Mean : 5.2118787878787876, Standard deviation : 27.30705061932881
Mean : 5.2236996834011755, Standard deviation : 27.30705061932881
(22445, 1) (22445,)
(16500, 1) (16500,)
(11055, 1) (11055,)

Standardize Essay_word_count

In [94]:
essay_scalar = StandardScaler()

essay_scalar.fit(x_train['essay_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
train_essay_word_count_standar = essay_scalar.transform(x_train['essay_word_count'].values.reshape(-1, 1))

essay_scalar.fit(x_train['essay_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
test_essay_word_count_standar = essay_scalar.transform(x_test['essay_word_count'].values.reshape(-1, 1))

essay_scalar.fit(x_cv['essay_word_count'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
cv_essay_word_count_standar = essay_scalar.transform(x_cv['essay_word_count'].values.reshape(-1, 1))

print(train_essay_word_count_standar.shape, y_train.shape)
print(test_essay_word_count_standar.shape, y_test.shape)
print(cv_essay_word_count_standar.shape, y_cv.shape)
(22445, 1) (22445,)
(16500, 1) (16500,)
(11055, 1) (11055,)

Standardize Positive Intensity

In [95]:
essay_scalar.fit(x_train['positive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
train_positive_standar = essay_scalar.transform(x_train['positive'].values.reshape(-1, 1))

essay_scalar.fit(x_train['positive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
test_positive_standar = essay_scalar.transform(x_test['positive'].values.reshape(-1, 1))

essay_scalar.fit(x_cv['positive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
cv_positive_standar = essay_scalar.transform(x_cv['positive'].values.reshape(-1, 1))

print(train_positive_standar.shape, y_train.shape)
print(test_positive_standar.shape, y_test.shape)
print(cv_positive_standar.shape, y_cv.shape)
(22445, 1) (22445,)
(16500, 1) (16500,)
(11055, 1) (11055,)

Standarsize Negitive Intensity

In [96]:
essay_scalar.fit(x_train['negitive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
train_negitive_standar = essay_scalar.transform(x_train['negitive'].values.reshape(-1, 1))

essay_scalar.fit(x_train['negitive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
test_negitive_standar = essay_scalar.transform(x_test['negitive'].values.reshape(-1, 1))

essay_scalar.fit(x_cv['negitive'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
cv_negitive_standar = essay_scalar.transform(x_cv['negitive'].values.reshape(-1, 1))

print(train_negitive_standar.shape, y_train.shape)
print(test_negitive_standar.shape, y_test.shape)
print(cv_negitive_standar.shape, y_cv.shape)
(22445, 1) (22445,)
(16500, 1) (16500,)
(11055, 1) (11055,)

Standardize Neutral Intensity

In [97]:
essay_scalar.fit(x_train['neutral'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
train_neutral_standar = essay_scalar.transform(x_train['neutral'].values.reshape(-1, 1))

essay_scalar.fit(x_train['neutral'].values.reshape(-1,1))
test_neutral_standar = essay_scalar.transform(x_test['neutral'].values.reshape(-1, 1))

essay_scalar.fit(x_cv['neutral'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
cv_neutral_standar = essay_scalar.transform(x_cv['neutral'].values.reshape(-1, 1))

print(train_neutral_standar.shape, y_train.shape)
print(test_neutral_standar.shape, y_test.shape)
print(cv_neutral_standar.shape, y_cv.shape)
(22445, 1) (22445,)
(16500, 1) (16500,)
(11055, 1) (11055,)

Assignment 8: DT

  1. Apply Decision Tree Classifier(DecisionTreeClassifier) on these feature sets
    • Set 1: categorical, numerical features + project_title(BOW) + preprocessed_eassay (BOW)
    • Set 2: categorical, numerical features + project_title(TFIDF)+ preprocessed_eassay (TFIDF)
    • Set 3: categorical, numerical features + project_title(AVG W2V)+ preprocessed_eassay (AVG W2V)
    • Set 4: categorical, numerical features + project_title(TFIDF W2V)+ preprocessed_eassay (TFIDF W2V)

  2. Hyper paramter tuning (best `depth` in range [1, 5, 10, 50, 100, 500, 100], and the best `min_samples_split` in range [5, 10, 100, 500])
    • Find the best hyper parameter which will give the maximum AUC value
    • Find the best hyper paramter using k-fold cross validation or simple cross validation data
    • Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning

  3. Graphviz
    • Visualize your decision tree with Graphviz. It helps you to understand how a decision is being made, given a new vector.
    • Since feature names are not obtained from word2vec related models, visualize only BOW & TFIDF decision trees using Graphviz
    • Make sure to print the words in each node of the decision tree instead of printing its index.
    • Just for visualization purpose, limit max_depth to 2 or 3 and either embed the generated images of graphviz in your notebook, or directly upload them as .png files.

  4. Representation of results
    • You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure
    • Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.
    • Along with plotting ROC curve, you need to print the confusion matrix with predicted and original labels of test data points
    • Once after you plot the confusion matrix with the test data, get all the `false positive data points`
      • Plot the WordCloud WordCloud
      • Plot the box plot with the `price` of these `false positive data points`
      • Plot the pdf with the `teacher_number_of_previously_posted_projects` of these `false positive data points`

  5. [Task-2]
    • Select 5k best features from features of Set 2 using`feature_importances_`, discard all the other remaining features and then apply any of the model of you choice i.e. (Dession tree, Logistic Regression, Linear SVM), you need to do hyperparameter tuning corresponding to the model you selected and procedure in step 2 and step 3

  6. Conclusion
    • You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link

Note: Data Leakage

  1. There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.
  2. To avoid the issue of data-leakage, make sure to split your data first and then vectorize it.
  3. While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.
  4. For more details please go through this link.

Decision Tree

2.1 DT On Set-1

Merging all Categorical and Numerical _ SET-1 BOW Encoding

In [183]:
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X_train = hstack((train_categories_one_hot,train_sub_categories_one_hot,train_state_one_hot,train_grade_one_hot,
                  train_teacher_one_hot,train_text_bow, train_titles_bow, train_quantity_standar,
                  train_prev_proj_standar, train_price_standar, train_title_word_count_standar, 
                  train_essay_word_count_standar, train_positive_standar, train_negitive_standar,
                  train_neutral_standar)).tocsr()
print(X_train.shape, y_train.shape)
print(type(X_train))
(22445, 10205) (22445,)
<class 'scipy.sparse.csr.csr_matrix'>
In [184]:
X_test = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
                  test_teacher_one_hot,test_text_bow, test_titles_bow, test_quantity_standar,
                  test_prev_proj_standar, test_price_standar, test_essay_word_count_standar, 
                  test_title_word_count_standar, test_positive_standar, test_negitive_standar,
                  test_neutral_standar)).tocsr()
print(X_test.shape, y_test.shape)
print(type(X_test))
(16500, 10205) (16500,)
<class 'scipy.sparse.csr.csr_matrix'>
In [185]:
X_cv = hstack((cv_categories_one_hot, cv_sub_categories_one_hot, cv_state_one_hot, cv_grade_one_hot,
                  cv_teacher_one_hot, cv_text_bow, cv_titles_bow, cv_quantity_standar,
                  cv_prev_proj_standar, cv_price_standar, cv_essay_word_count_standar, 
                  cv_title_word_count_standar, cv_positive_standar, cv_negitive_standar,
                  cv_neutral_standar)).tocsr()
print(X_cv.shape, y_cv.shape)
print(type(X_cv))
(11055, 10205) (11055,)
<class 'scipy.sparse.csr.csr_matrix'>
In [186]:
print(X_train.shape, y_train.shape)
(22445, 10205) (22445,)

Hyperparameter Tunning GridSearch

In [187]:
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
import seaborn as sea
In [188]:
DT = DecisionTreeClassifier()

parameters = {'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]}

classifier = GridSearchCV(DT, parameters, cv=3, scoring='roc_auc')
classifier.fit(X_train, y_train)
Out[188]:
GridSearchCV(cv=3, error_score='raise-deprecating',
       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best'),
       fit_params=None, iid='warn', n_jobs=None,
       param_grid={'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring='roc_auc', verbose=0)
In [189]:
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_min_samples_split', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]

fig, ax = plt.subplots(1,2, figsize=(20,6))

sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('CV Set')

plt.show()

Trainng Model Using Best HyperParameter

In [190]:
def batch_predict(clf, data):
    # roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
    # not the predicted outputs
    
    y_data_pred = []
    tr_loop = data.shape[0] - data.shape[0]%1000
    # consider you X_tr shape is 49041, then your cr_loop will be 49041 - 49041%1000 = 49000
    # in this for loop we will iterate unti the last 1000 multiplier
    for i in range(0, tr_loop, 1000):
        y_data_pred.extend(clf.predict_proba(data[i:i+1000])[:,1])
    # we will be predicting for the last data points
    y_data_pred.extend(clf.predict_proba(data[tr_loop:])[:,1])
    
    return y_data_pred
In [192]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier

classifier = DecisionTreeClassifier(max_depth = 100, min_samples_split = 500)
classifier_viz = DecisionTreeClassifier(max_depth = 3, min_samples_split = 500)

classifier.fit(X_train, y_train)
classifier_viz.fit(X_train, y_train)

y_train_pred = batch_predict(classifier,X_train)    
y_test_pred = batch_predict(classifier_viz,X_test)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)

plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()

Confusion Matrix for Train and Test Data

In [193]:
def predict(proba, threshould, fpr, tpr):
    
    t = threshould[np.argmax(fpr*(1-tpr))]
    print("the maximum value of tpr*(1-fpr)", max(tpr*(1-fpr)), "for threshold", np.round(t,3))
    predictions = []
    global predictions1
    for i in proba:
        if i>=t:
            predictions.append(1)
        else:
            predictions.append(0)
    predictions1 = predictions
    return predictions
In [194]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns; sns.set()

con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))

key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5)) 

labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)

sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('Test Set')

plt.show()
the maximum value of tpr*(1-fpr) 0.7167249621379379 for threshold 0.719
the maximum value of tpr*(1-fpr) 0.37269325393130454 for threshold 0.808

Tree Visualization

In [199]:
BOW_feature = cat_feat + sub_cat_feat + grade_feat + school_feat + teacher_feat + BOW_feat_essay + BOW_feat_title
#BOW_feature.append('price')
#BOW_feature.append('teacher_number_of_previously_posted_projects')
#BOW_feature.append('quantity')
#BOW_feature.append('title_word_count')
#BOW_feature.append('essay_word_count')
#BOW_feature.append('positive')
#BOW_feature.append('negitive')

len(BOW_feature)
Out[199]:
10205
In [201]:
TFIDF_feature = cat_feat + sub_cat_feat + grade_feat + school_feat + teacher_feat + BOW_feat_essay + BOW_feat_title
#TFIDF_feature.append('price')
#TFIDF_feature.append('teacher_number_of_previously_posted_projects')
#TFIDF_feature.append('quantity')
#TFIDF_feature.append('title_word_count')
#TFIDF_feature.append('essay_word_count')
#TFIDF_feature.append('positive')
#TFIDF_feature.append('negitive')
len(TFIDF_feature)
Out[201]:
10205
In [197]:
from sklearn.externals.six import StringIO  
from IPython.display import Image  
from sklearn.tree import export_graphviz
import pydotplus

BOW encoded Decision Tree

In [200]:
dot_data = StringIO()

export_graphviz(classifier_viz, out_file=dot_data, filled=True, rounded=True, special_characters=True, feature_names=BOW_feature)
                
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
Image(graph.create_png())
Out[200]:

TFIDF encoded Decision Tree

In [202]:
dot_data = StringIO()

export_graphviz(classifier_viz, out_file=dot_data, filled=True, rounded=True, special_characters=True, feature_names=TFIDF_feature)
                
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
Image(graph.create_png())
Out[202]:

False Positive Word Cloud

In [244]:
bow_test = test_text_bow.todense()
print(bow_test.shape)
len(BOW_feat_essay)
(16500, 8869)
Out[244]:
8869
In [247]:
y_test_list = list(y_test[::])
len(y_test_list)
Out[247]:
16500
In [245]:
fp_index = []
fp_count = 0

for i in range(len(y_test_pred)):
    if y_test_list[i] == 0 and y_test_pred[i] <= 0.9:
        fp_index.append(i)
        fp_count = fp_count + 1 
    else :
        pass
print(fp_count)
2491
In [268]:
fp_index[0:10]
Out[268]:
[0, 2, 3, 9, 14, 15, 20, 23, 26, 43]
In [269]:
df1 = pd.DataFrame(bow_test) 
In [274]:
df1_final = df1.iloc[fp_index,:]
print(df1_final.shape)
(2491, 8869)
In [275]:
best_indices = []

for j in range(8869):    
    s = df1_final[j].sum()
    if s >= 100 :
        best_indices.append(j)
    else :
        continue    
In [276]:
len(best_indices)
Out[276]:
591
In [277]:
best_indices[0:10] 
Out[277]:
[3, 45, 105, 161, 162, 163, 182, 183, 194, 234]
In [279]:
fp_words = []

for a in best_indices :
    fp_words.append(str(BOW_feature[a]))
In [280]:
fp_words[0:10]
Out[280]:
['Music_Arts', 'WY', '10th', '31', '32', '320', '43', '44', '504', '81']
In [285]:
from wordcloud import WordCloud
from wordcloud import STOPWORDS 
#convert list to string and generate
words_string=(" ").join(fp_words)
wordcloud = WordCloud(width = 1000, height = 500).generate(words_string)
plt.figure(figsize=(25,10))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

Box & PDF plot for False Positives

In [222]:
df = pd.DataFrame(x_test['price'])
print(df.head(2))
    price
0  337.18
1   89.49
In [262]:
plt.figure(figsize=(15,3))
df1 = df.iloc[fp_index, : ]
sea.boxplot(df1.values)
plt.title("Rejected Projects that Predicted as Positive")
plt.ylabel("Box plot for False Positives")
plt.xlabel("Price")
plt.show()
In [263]:
plt.figure(figsize=(15,3))
sns.distplot(df1.values, label="PDF of False Positives")
plt.title('Teacher_number_of_previously_posted_projects False Positives PDF')
plt.xlabel('Teacher_number_of_previously_posted_projects')
plt.legend()
plt.show()

2.2 Merging all Categorical and Numerical _ SET-2 TF-IDF Encoding

In [207]:
X_train = hstack((train_categories_one_hot, train_sub_categories_one_hot, train_state_one_hot, train_grade_one_hot,
                  train_teacher_one_hot,train_text_tfidf, train_title_tfidf, train_quantity_standar,
                  train_prev_proj_standar, train_price_standar, train_title_word_count_standar, 
                  train_essay_word_count_standar, train_positive_standar, train_negitive_standar,
                  train_neutral_standar)).tocsr()
print(X_train.shape, y_train.shape)
print(type(X_train))
(22445, 10205) (22445,)
<class 'scipy.sparse.csr.csr_matrix'>
In [208]:
X_test = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
                  test_teacher_one_hot,test_text_tfidf, test_title_tfidf, test_quantity_standar,
                  test_prev_proj_standar, test_price_standar,test_title_word_count_standar, 
                  test_essay_word_count_standar, test_positive_standar, test_negitive_standar,
                  test_neutral_standar)).tocsr()
print(X_test.shape, y_test.shape)
print(type(X_test))
(16500, 10205) (16500,)
<class 'scipy.sparse.csr.csr_matrix'>
In [209]:
X_cv = hstack((cv_categories_one_hot,cv_sub_categories_one_hot,cv_state_one_hot,cv_grade_one_hot,
                  cv_teacher_one_hot,cv_text_tfidf, cv_title_tfidf, cv_quantity_standar,
                  cv_prev_proj_standar, cv_price_standar, cv_title_word_count_standar, 
                  cv_essay_word_count_standar, cv_positive_standar, cv_negitive_standar,
                  cv_neutral_standar)).tocsr()
print(X_cv.shape, y_cv.shape)
print(type(X_cv))
(11055, 10205) (11055,)
<class 'scipy.sparse.csr.csr_matrix'>

Hyperparameter Tunning

In [210]:
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
DT = DecisionTreeClassifier()

parameters = {'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]}

classifier_5 = GridSearchCV(DT, parameters, cv=3, scoring='roc_auc')
classifier_5.fit(X_train, y_train)
Out[210]:
GridSearchCV(cv=3, error_score='raise-deprecating',
       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best'),
       fit_params=None, iid='warn', n_jobs=None,
       param_grid={'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring='roc_auc', verbose=0)
In [107]:
import seaborn as sea
max_scores = pd.DataFrame(classifier_5.cv_results_).groupby(['param_min_samples_split', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]

fig, ax = plt.subplots(1,2, figsize=(20,6))
sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('CV Set')

plt.show()

Training Model using best Hyperparameter

In [119]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier

classifier = DecisionTreeClassifier(max_depth = 50, min_samples_split = 500)
classifier_viz = DecisionTreeClassifier(max_depth = 3, min_samples_split = 500)

classifier.fit(X_train, y_train)
classifier_viz.fit(X_train, y_train)

y_train_pred = batch_predict(classifier,X_train)    
y_test_pred = batch_predict(classifier_viz,X_test)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)

plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()

Confusion Matrix For Train & Test Data

In [266]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns; sns.set()

con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))

key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5)) 

labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)

sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('Test Set')

plt.show()
the maximum value of tpr*(1-fpr) 0.6601800638950536 for threshold 0.927
the maximum value of tpr*(1-fpr) 0.33947756296330406 for threshold 0.873

Tree Visualization

In [120]:
from sklearn.externals.six import StringIO  
from IPython.display import Image  
from sklearn.tree import export_graphviz
import pydotplus
In [121]:
dot_data = StringIO()

export_graphviz(classifier_viz, out_file=dot_data, filled=True, rounded=True, special_characters=True, feature_names=BOW_feature)
                
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
Image(graph.create_png())
Out[121]:

False Positive Word Cloud

In [123]:
bow_test = test_text_bow.todense()
print(bow_test.shape)
len(BOW_feat_essay)
(16500, 8869)
Out[123]:
8869
In [124]:
y_test_list = list(y_test[::])
len(y_test_list)
Out[124]:
16500
In [125]:
fp_index = []
fp_count = 0

for i in range(len(y_test_pred)):
    if y_test_list[i] == 0 and y_test_pred[i] <= 0.9:
        fp_index.append(i)
        fp_count = fp_count + 1 
    else :
        pass
print(fp_count)
print(fp_index[0:10])
2491
[0, 2, 3, 9, 14, 15, 20, 23, 26, 43]
In [126]:
df1 = pd.DataFrame(bow_test) 
df1_final = df1.iloc[fp_index,:]
print(df1_final.shape)
(2491, 8869)
In [127]:
best_indices = []

for j in range(8869):    
    s = df1_final[j].sum()
    if s >= 100 :
        best_indices.append(j)
    else :
        continue   
print(len(best_indices))
print(best_indices[0:10])
591
[3, 45, 105, 161, 162, 163, 182, 183, 194, 234]
In [128]:
fp_words = []

for a in best_indices :
    fp_words.append(str(BOW_feature[a]))
print(fp_words[0:10])
['Music_Arts', 'WY', '10th', '31', '32', '320', '43', '44', '504', '81']
In [129]:
from wordcloud import WordCloud
from wordcloud import STOPWORDS 
#convert list to string and generate
words_string=(" ").join(fp_words)
wordcloud = WordCloud(width = 1000, height = 500).generate(words_string)
plt.figure(figsize=(25,10))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

Box & PDF plot for False Positives

In [130]:
df = pd.DataFrame(x_test['price'])
print(df.head(2))
    price
0  337.18
1   89.49
In [131]:
plt.figure(figsize=(15,3))
df1 = df.iloc[fp_index, : ]
sea.boxplot(df1.values)
plt.title("Rejected Projects that Predicted as Positive")
plt.ylabel("Box plot for False Positives")
plt.xlabel("Price")
plt.show()
In [132]:
plt.figure(figsize=(15,3))
sns.distplot(df1.values, label="PDF of False Positives")
plt.title('Teacher_number_of_previously_posted_projects False Positives PDF')
plt.xlabel('Teacher_number_of_previously_posted_projects')
plt.legend()
plt.show()
In [ ]:
 

2.3 Merging all Categorical and Numerical _ SET-3 AVG-W2V Encoding

In [133]:
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X_train = hstack((train_categories_one_hot,train_sub_categories_one_hot,train_state_one_hot,train_grade_one_hot,
                  train_teacher_one_hot, train_title_avg_w2v_vectors, train_avg_w2v_vectors, train_quantity_standar,
                  train_prev_proj_standar, train_price_standar, train_positive_standar, train_negitive_standar,
                  train_neutral_standar, train_title_word_count_standar, train_essay_word_count_standar)).tocsr()
print(X_train.shape, y_train.shape)
print(type(X_train))
(22445, 707) (22445,)
<class 'scipy.sparse.csr.csr_matrix'>
In [134]:
X_test = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
                  test_teacher_one_hot, test_title_avg_w2v_vectors, test_avg_w2v_vectors, test_quantity_standar,
                  test_prev_proj_standar, test_price_standar, test_positive_standar, test_negitive_standar,
                  test_neutral_standar, test_title_word_count_standar, test_essay_word_count_standar)).tocsr()
print(X_test.shape, y_test.shape)
print(type(X_test))
(16500, 707) (16500,)
<class 'scipy.sparse.csr.csr_matrix'>
In [135]:
X_cv = hstack((cv_categories_one_hot, cv_sub_categories_one_hot, cv_state_one_hot, cv_grade_one_hot,
                  cv_teacher_one_hot, cv_title_avg_w2v_vectors, cv_avg_w2v_vectors, cv_quantity_standar,
                  cv_prev_proj_standar, cv_price_standar, cv_positive_standar, cv_negitive_standar,
                  cv_neutral_standar, cv_title_word_count_standar, cv_essay_word_count_standar)).tocsr()
print(X_cv.shape, y_cv.shape)
print(type(X_cv))
(11055, 707) (11055,)
<class 'scipy.sparse.csr.csr_matrix'>
In [136]:
print(X_train1.shape, y_train.shape)
(49041, 708) (49041,)

Hyperparameter Tunning

In [136]:
DT = DecisionTreeClassifier()

parameters = {'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]}

classifier = GridSearchCV(DT, parameters, cv=3, scoring='roc_auc')
classifier.fit(X_train, y_train)
Out[136]:
GridSearchCV(cv=3, error_score='raise-deprecating',
       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best'),
       fit_params=None, iid='warn', n_jobs=None,
       param_grid={'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring='roc_auc', verbose=0)
In [137]:
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_min_samples_split', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]

fig, ax = plt.subplots(1,2, figsize=(20,6))

sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('CV Set')

plt.show()

Training Model Using Hyperparameter

In [138]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier

classifier = DecisionTreeClassifier(max_depth = 50, min_samples_split = 500)
classifier_viz = DecisionTreeClassifier(max_depth = 3, min_samples_split = 500)

classifier.fit(X_train, y_train)
classifier_viz.fit(X_train, y_train)

y_train_pred = batch_predict(classifier,X_train)    
y_test_pred = batch_predict(classifier_viz,X_test)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)

plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()

Train & Test Confusion Matrix

In [139]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns; sns.set()

con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))

key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5)) 

labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)

sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('Test Set')

plt.show()
the maximum value of tpr*(1-fpr) 0.5957659844198493 for threshold 0.75
the maximum value of tpr*(1-fpr) 0.3397072481785906 for threshold 0.867

False Positive Words Clouds

In [141]:
bow_test = test_text_bow.todense()
print(bow_test.shape)
len(BOW_feat_essay)
(16500, 8869)
Out[141]:
8869
In [142]:
y_test_list = list(y_test[::])
len(y_test_list)
Out[142]:
16500
In [143]:
fp_index = []
fp_count = 0

for i in range(len(y_test_pred)):
    if y_test_list[i] == 0 and y_test_pred[i] <= 0.9:
        fp_index.append(i)
        fp_count = fp_count + 1 
    else :
        pass
print(fp_count)
print(fp_index[0:10])
2256
[0, 3, 9, 14, 15, 20, 23, 26, 43, 48]
In [144]:
df1 = pd.DataFrame(bow_test) 
df1_final = df1.iloc[fp_index,:]
print(df1_final.shape)
(2256, 8869)
In [145]:
best_indices = []

for j in range(8869):    
    s = df1_final[j].sum()
    if s >= 100 :
        best_indices.append(j)
    else :
        continue   
print(len(best_indices))
print(best_indices[0:10])
554
[3, 105, 161, 162, 163, 182, 194, 234, 255, 259]
In [146]:
fp_words = []

for a in best_indices :
    fp_words.append(str(BOW_feature[a]))
print(fp_words[0:10])
['Music_Arts', '10th', '31', '32', '320', '43', '504', '81', '99', 'abcya']
In [147]:
from wordcloud import WordCloud
from wordcloud import STOPWORDS 
#convert list to string and generate
words_string=(" ").join(fp_words)
wordcloud = WordCloud(width = 1000, height = 500).generate(words_string)
plt.figure(figsize=(25,10))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

Box & PDF Plot for False Positive

In [148]:
df = pd.DataFrame(x_test['price'])
print(df.head(2))
    price
0  337.18
1   89.49
In [149]:
plt.figure(figsize=(15,3))
df1 = df.iloc[fp_index, : ]
sea.boxplot(df1.values)
plt.title("Rejected Projects that Predicted as Positive")
plt.ylabel("Box plot for False Positives")
plt.xlabel("Price")
plt.show()
In [150]:
plt.figure(figsize=(15,3))
sns.distplot(df1.values, label="PDF of False Positives")
plt.title('Teacher_number_of_previously_posted_projects False Positives PDF')
plt.xlabel('Teacher_number_of_previously_posted_projects')
plt.legend()
plt.show()

2.4 Merging all Categorical and Numerical _ SET-4 TFIDF-W2V Encoding

In [161]:
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X_train1 = hstack((train_categories_one_hot,train_sub_categories_one_hot,train_state_one_hot,train_grade_one_hot,
                   train_teacher_one_hot, train_title_tfidf_w2v_vectors, train_essay_tfidf_w2v_vectors,
                   train_quantity_standar, train_prev_proj_standar, train_price_standar,train_positive_standar,
                   train_negitive_standar,  train_neutral_standar, train_title_word_count_standar, 
                   train_essay_word_count_standar)).tocsr()
print(X_train1.shape, y_train.shape)
print(type(X_train1)) #train_title_tfidf_w2v_vectors train_essay_tfidf_w2v_vectors
(22445, 707) (22445,)
<class 'scipy.sparse.csr.csr_matrix'>
In [162]:
X_test1 = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
                  test_teacher_one_hot, test_title_tfidf_w2v_vectors, test_essay_tfidf_w2v_vectors,
                  test_quantity_standar, test_prev_proj_standar, test_price_standar, test_positive_standar,
                  test_negitive_standar, test_neutral_standar, test_title_word_count_standar, 
                  test_essay_word_count_standar)).tocsr()
print(X_test1.shape, y_test.shape)
print(type(X_test1)) #train_title_tfidf_w2v_vectors train_essay_tfidf_w2v_vectors
(16500, 707) (16500,)
<class 'scipy.sparse.csr.csr_matrix'>
In [163]:
X_cv1 = hstack((cv_categories_one_hot, cv_sub_categories_one_hot, cv_state_one_hot, cv_grade_one_hot,
                   cv_teacher_one_hot, cv_title_tfidf_w2v_vectors, cv_essay_tfidf_w2v_vectors,
                   cv_quantity_standar, cv_prev_proj_standar, cv_price_standar,cv_positive_standar,
                   cv_negitive_standar, cv_neutral_standar, cv_title_word_count_standar, 
                   cv_essay_word_count_standar)).tocsr()
print(X_cv1.shape, y_cv.shape)
print(type(X_cv1)) #train_title_tfidf_w2v_vectors train_essay_tfidf_w2v_vectors
(11055, 707) (11055,)
<class 'scipy.sparse.csr.csr_matrix'>
In [164]:
print(X_train.shape, y_train.shape)
(22445, 707) (22445,)

Hyperparameter Tunning

In [165]:
DT = DecisionTreeClassifier()

parameters = {'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]}

classifier = GridSearchCV(DT, parameters, cv=3, scoring='roc_auc')
classifier.fit(X_train, y_train)
Out[165]:
GridSearchCV(cv=3, error_score='raise-deprecating',
       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best'),
       fit_params=None, iid='warn', n_jobs=None,
       param_grid={'max_depth': [1, 5, 10, 50, 100, 500], 'min_samples_split': [5, 10, 100, 500]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
       scoring='roc_auc', verbose=0)
In [167]:
max_scores = pd.DataFrame(classifier.cv_results_).groupby(['param_min_samples_split', 'param_max_depth']).max().unstack()[['mean_test_score', 'mean_train_score']]

fig, ax = plt.subplots(1,2, figsize=(20,6))

sea.heatmap(max_scores.mean_train_score, annot = True, fmt='.4g', ax=ax[0])
sea.heatmap(max_scores.mean_test_score, annot = True, fmt='.4g', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('CV Set')

plt.show()

Training Model using Best Hyperparameter

In [170]:
# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier

classifier = DecisionTreeClassifier(max_depth = 10, min_samples_split = 500)
classifier_viz = DecisionTreeClassifier(max_depth = 3, min_samples_split = 500)

classifier.fit(X_train, y_train)
classifier_viz.fit(X_train, y_train)

y_train_pred = batch_predict(classifier,X_train)    
y_test_pred = batch_predict(classifier_viz,X_test)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)

plt.plot(train_fpr, train_tpr, label="Train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="Test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("True Positive Rate(TPR)")
plt.ylabel("False Positive Rate(FPR)")
plt.title("AUC")
plt.grid()
plt.show()

Train & Test Confusion Matrix

In [171]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns; sns.set()

con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))

key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5)) 

labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)

sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('Test Set')

plt.show()
the maximum value of tpr*(1-fpr) 0.4758952148627332 for threshold 0.902
the maximum value of tpr*(1-fpr) 0.3397072481785906 for threshold 0.867

False Positive Word Cloud

In [173]:
bow_test = test_text_bow.todense()
print(bow_test.shape)
len(BOW_feat_essay)
(16500, 8869)
Out[173]:
8869
In [174]:
y_test_list = list(y_test[::])
len(y_test_list)
Out[174]:
16500
In [175]:
fp_index = []
fp_count = 0

for i in range(len(y_test_pred)):
    if y_test_list[i] == 0 and y_test_pred[i] <= 0.9:
        fp_index.append(i)
        fp_count = fp_count + 1 
    else :
        pass
print(fp_count)
print(fp_index[0:10])
2256
[0, 3, 9, 14, 15, 20, 23, 26, 43, 48]
In [176]:
df1 = pd.DataFrame(bow_test) 
df1_final = df1.iloc[fp_index,:]
print(df1_final.shape)
(2256, 8869)
In [177]:
best_indices = []

for j in range(8869):    
    s = df1_final[j].sum()
    if s >= 100 :
        best_indices.append(j)
    else :
        continue   
print(len(best_indices))
print(best_indices[0:10])
554
[3, 105, 161, 162, 163, 182, 194, 234, 255, 259]
In [178]:
fp_words = []

for a in best_indices :
    fp_words.append(str(BOW_feature[a]))
print(fp_words[0:10])
['Music_Arts', '10th', '31', '32', '320', '43', '504', '81', '99', 'abcya']
In [179]:
from wordcloud import WordCloud
from wordcloud import STOPWORDS 
#convert list to string and generate
words_string=(" ").join(fp_words)
wordcloud = WordCloud(width = 1000, height = 500).generate(words_string)
plt.figure(figsize=(25,10))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

Box & PDF plot for False Positives

In [180]:
df = pd.DataFrame(x_test['price'])
print(df.head(2))
    price
0  337.18
1   89.49
In [181]:
plt.figure(figsize=(15,3))
df1 = df.iloc[fp_index, : ]
sea.boxplot(df1.values)
plt.title("Rejected Projects that Predicted as Positive")
plt.ylabel("Box plot for False Positives")
plt.xlabel("Price")
plt.show()
In [182]:
plt.figure(figsize=(15,3))
sns.distplot(df1.values, label="PDF of False Positives")
plt.title('Teacher_number_of_previously_posted_projects False Positives PDF')
plt.xlabel('Teacher_number_of_previously_posted_projects')
plt.legend()
plt.show()

2.5 Apply Decision Tree on Set-5

[Task-2] Select 5k best features from features of Set 2 usingfeature_importances_, discard all the other remaining features and then apply any of the model of you choice i.e. (Dession tree, Logistic Regression, Linear SVM), you need to do hyperparameter tuning corresponding to the model you selected and procedure in step 2 and step 3 </br> </br>

In [211]:
X_train1 = hstack((train_categories_one_hot, train_sub_categories_one_hot, train_state_one_hot, train_grade_one_hot,
                  train_teacher_one_hot,train_text_tfidf, train_title_tfidf, train_quantity_standar,
                  train_prev_proj_standar, train_price_standar, train_title_word_count_standar, 
                  train_essay_word_count_standar, train_positive_standar, train_negitive_standar,
                  train_neutral_standar)).tocsr()
print(X_train1.shape, y_train.shape)
print(type(X_train1))
(22445, 10205) (22445,)
<class 'scipy.sparse.csr.csr_matrix'>
In [212]:
X_test1 = hstack((test_categories_one_hot,test_sub_categories_one_hot,test_state_one_hot,test_grade_one_hot,
                  test_teacher_one_hot,test_text_tfidf, test_title_tfidf, test_quantity_standar,
                  test_prev_proj_standar, test_price_standar,test_title_word_count_standar, 
                  test_essay_word_count_standar, test_positive_standar, test_negitive_standar,
                  test_neutral_standar)).tocsr()
print(X_test1.shape, y_test.shape)
print(type(X_test1))
(16500, 10205) (16500,)
<class 'scipy.sparse.csr.csr_matrix'>
In [215]:
X_cv1 = hstack((cv_categories_one_hot,cv_sub_categories_one_hot,cv_state_one_hot,cv_grade_one_hot,
                  cv_teacher_one_hot,cv_text_tfidf, cv_title_tfidf, cv_quantity_standar,
                  cv_prev_proj_standar, cv_price_standar, cv_title_word_count_standar, 
                  cv_essay_word_count_standar, cv_positive_standar, cv_negitive_standar,
                  cv_neutral_standar)).tocsr()
print(X_cv1.shape, y_cv.shape)
print(type(X_cv1))
(11055, 10205) (11055,)
<class 'scipy.sparse.csr.csr_matrix'>
In [221]:
# https://datascience.stackexchange.com/questions/6683/feature-selection-using-feature-importances-in-random-forests-with-scikit-learn
https://stackoverflow.com/questions/47111434/randomforestregressor-and-feature-importances-error
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
def selectKImportance(model, X, k=5):
     return X[:,model.best_estimator_.feature_importances_.argsort()[::-1][:k]]
In [222]:
X_train1_best = selectKImportance(classifier_5, X_train1, 5000)
X_test1_best = selectKImportance(classifier_5, X_test1, 5000)
In [224]:
print(X_train1_best.shape)
print(X_test1_best.shape)
(22445, 5000)
(16500, 5000)

Hyperparameter Tunning

In [225]:
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression

LG = LogisticRegression()

parameters = {'C': [10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1, 10**2, 10**3, 10**4]}

classif = GridSearchCV(LG, parameters, cv=7, scoring='roc_auc')
classifier = classif.fit(X_train1_best, y_train)

train_auc= classif.cv_results_['mean_train_score']
train_auc_std= classif.cv_results_['std_train_score']
cv_auc = classif.cv_results_['mean_test_score'] 
cv_auc_std = clf.cv_results_['std_test_score']

plt.plot(parameters['C'], train_auc, label='Train AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(parameters['C'],train_auc - train_auc_std,train_auc + train_auc_std,alpha=0.2,color='darkblue')

plt.plot(parameters['C'], cv_auc, label='CV AUC')
# this code is copied from here: https://stackoverflow.com/a/48803361/4084039
plt.gca().fill_between(parameters['C'],cv_auc - cv_auc_std, cv_auc + cv_auc_std,alpha=0.2,color='darkorange')

plt.scatter(parameters['C'], train_auc, label='Train AUC points')
plt.scatter(parameters['C'], cv_auc, label='CV AUC points')

plt.xscale('log')
plt.legend()
plt.xlabel("K: hyperparameter")
plt.ylabel("AUC")
plt.title("ERROR PLOTS")
plt.grid(True)
plt.show()
In [234]:
from sklearn.metrics import roc_curve, auc


classif_5 = LogisticRegression(C=1, penalty='l1');
classif_5.fit(X_train1_best, y_train)

y_train_pred = batch_predict(classif_5, X_train1_best)    
y_test_pred = batch_predict(classif_5, X_test1_best)

train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)
test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)

plt.plot(train_fpr, train_tpr, label="train AUC ="+str(auc(train_fpr, train_tpr)))
plt.plot(test_fpr, test_tpr, label="test AUC ="+str(auc(test_fpr, test_tpr)))
plt.legend()
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("ERROR PLOTS")
plt.grid(True)
plt.show()

Confusion Matrix

In [228]:
#https://www.quantinsti.com/blog/creating-heatmap-using-python-seaborn
import seaborn as sns; sns.set()

con_m_train = confusion_matrix(y_train, predict(y_train_pred, tr_thresholds, train_fpr, train_tpr))
con_m_test = confusion_matrix(y_test, predict(y_test_pred, te_thresholds, test_fpr, test_tpr))

key = (np.asarray([['TN','FP'], ['FN', 'TP']]))
fig, ax = plt.subplots(1,2, figsize=(12,5)) 

labels_train = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_train.flatten())])).reshape(2,2)
labels_test = (np.asarray(["{0} = {1:.2f}" .format(key, value) for key, value in zip(key.flatten(), con_m_test.flatten())])).reshape(2,2)

sns.heatmap(con_m_train, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_train, fmt = '', ax=ax[0])
sns.heatmap(con_m_test, linewidths=.5, xticklabels=['PREDICTED : NO', 'PREDICTED : YES'], yticklabels=['ACTUAL : NO', 'ACTUAL : YES'], annot = labels_test, fmt = '', ax=ax[1])

ax[0].set_title('Train Set')
ax[1].set_title('Test Set')

plt.show()
the maximum value of tpr*(1-fpr) 0.47702875679021484 for threshold 0.831
the maximum value of tpr*(1-fpr) 0.4218306902457305 for threshold 0.847

3. Conclusion

In [240]:
# Please compare all your models using Prettytable library
# http://zetcode.com/python/prettytable/
from prettytable import PrettyTable
TB = PrettyTable()
TB.field_names = ["Model", "Hyperparameter", "Train_AUC", "Test_Auc"]
TB.title = "Decision Tree"
TB.add_row(["BOW-Model", "Depth:100 | Samp_Split:500", 0.92,0.68])
TB.add_row(["TFIDF-Model", "Depth:50 | Samp_Split:500", 0.88, 0.62])
TB.add_row(["AvgW2v-Model", "Depth:100 | Samp_Split:500", 0.85, 0.63])
TB.add_row(["Tf-Idf-Model", "Depth:10 | Samp_Split:500", 0.74, 0.63])
TB.add_row(["Logistic Reg on Best 5K","C:1.0  | Reg: L1", 0.75, 0.69])
print(TB)
+-------------------------+----------------------------+-----------+----------+
|          Model          |       Hyperparameter       | Train_AUC | Test_Auc |
+-------------------------+----------------------------+-----------+----------+
|        BOW-Model        | Depth:100 | Samp_Split:500 |    0.92   |   0.68   |
|       TFIDF-Model       | Depth:50 | Samp_Split:500  |    0.88   |   0.62   |
|       AvgW2v-Model      | Depth:100 | Samp_Split:500 |    0.85   |   0.63   |
|       Tf-Idf-Model      | Depth:10 | Samp_Split:500  |    0.74   |   0.63   |
| Logistic Reg on Best 5K |      C:1.0  | Reg: L1      |    0.75   |   0.69   |
+-------------------------+----------------------------+-----------+----------+